Power Token Balancing: Adapting CMPs to Power Constraints for Parallel Multithreaded Workloads

Abstract

In the recent years virtually all processor architectures employ multiple cores per chip (CMPs). It is possible to use legacy (i.e., single-core) power saving techniques in CMPs which run either sequential applications or independent multithreaded workloads. However, new challenges arise when running parallel shared-memory applications. In the later case, sacrificing some performance in a single core (thread) in order to be more energy-efficient might unintentionally delay the rest of cores (threads) due to synchronization points (locks/barriers), therefore, harming the performance of the whole application. CMPs increasingly face thermal and power-related problems during their typical use. Such problems can be solved by setting a power budget to the processor/core. This paper initially studies the behavior of different techniques to match a predefined power budget in a CMP processor. While legacy techniques properly work for thread independent/multi-programmed workloads, parallel workloads exhibit the problem of independently adapting the power of each core in a thread dependent scenario. In order to solve this problem we propose a novel mechanism, Power Token Balancing (PTB), aimed at accurately matching an external power constraint by balancing the power consumed among the different cores using a power token-based approach while optimizing the energy efficiency. We can use power (seen as tokens or coupons) from non-critical threads for the benefit of critical threads. PTB runs transparent for thread independent / multiprogrammed workloads and can be also used as a spin lock detector based on power patterns. Results show that PTB matches more accurately a predefined power budget (total energy consumed over the budget is reduced to 8\% for a 16-core CMP) than DVFS with only a 3\% energy increase. Finally, we can trade accuracy on matching the power budget for energy-efficiency reducing the energy a 4% with a 20% of accuracy.